Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/50908

TitleAnalysing the correlation between social network analysis measures and performance of students in social network-based engineering education
Author(s)Putnik, Goran D.
Costa, Eric
Alves, Catia
Castro, Helio
Varela, Maria Leonilde Rocha
Shah, Vaibhav
KeywordsSocial network-based engineering education
Education 3.0
Project-led education
Social networks
Social network analysis
Students' grades
Correlation
Issue date2016
PublisherSpringer
JournalInternational Journal of Technology and Design Education
Abstract(s)Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment, considering two different dimensions: (1) to organize the education process as a social network-based process; and (2) to analyze the students' interactions in the context of evaluation of the students learning performance. The objective of this paper is to present a new model for students evaluation based on their behavior during the course and its validation in comparison with the traditional model of students' evaluation. The validation of the new evaluation model is made through an analysis of the correlation between social network analysis measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and average tie strength) and the grades obtained by students (grades for quality of work, grades for volume of work, grades for diversity of work, and final grades) in a social network-based engineering education. The main finding is that the obtained correlation results can be used to make the process of the students' performance evaluation based on students interactions (behavior) analysis, to make the evaluation partially automatic, increasing the objectivity and productivity of teachers and allowing a more scalable process of evaluation. The results also contribute to the behavioural theory of learning performance evaluation. More specific findings related to the correlation analysis are: (1) the more different interactions a student had (degree centrality) and the more frequently the student was between the interaction paths of other students (betweenness centrality), the better was the quality of the work; (2) all five social network measures had a positive and strong correlation with the grade for volume of work and with the final gra
TypeArticle
URIhttps://hdl.handle.net/1822/50908
DOI10.1007/s10798-015-9318-z
ISSN0957-7572
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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